大模型面对模糊输入仍易胡说，但加个提示就能大幅提高可靠性。
Are vision language models robust to uncertain inputs?
- 让模型在不确定时主动拒绝回答，显著提升鲁棒性。
- 在ImageNet上用此方法可实现接近完美的抗不确定性表现。
- 提出基于图像描述多样性检测模型内部不确定性的新机制。
深度学习模型在应对不确定和模糊输入时的鲁棒性是一个关键挑战。尽管大规模视觉语言模型（如GPT4o）的进展暗示增大模型和训练数据规模可缓解该问题，但我们的实证评估揭示了更复杂的图景。通过两个经典的不确定性量化任务——异常检测与在固有模糊条件下的分类，我们发现较新的大型VLM相比早期模型确有更强鲁棒性，但仍倾向于严格遵循指令，导致在面对不明确或异常输入时仍产生自信的幻觉回应。值得注意的是，在自然图像（如ImageNet）上，仅通过提示模型在不确定时放弃预测，即可实现显著的可靠性提升，多个场景下达到近乎完美的鲁棒性。然而，在星系形态分类等特定领域任务中，由于缺乏专业知识，模型难以进行可靠的风险估计。最后，我们提出一种基于描述多样性的新机制，可在无需标注数据的情况下预测模型是否能成功拒绝预测，从而揭示其内部不确定性。
原文摘要 · Abstract (English)
Robustness against uncertain and ambiguous inputs is a critical challenge for deep learning models. While recent advancements in large scale vision language models (VLMs, e.g. GPT4o) might suggest that increasing model and training dataset size would mitigate this issue, our empirical evaluation shows a more complicated picture. Testing models using two classic uncertainty quantification tasks, anomaly detection and classification under inherently ambiguous conditions, we find that newer and larger VLMs indeed exhibit improved robustness compared to earlier models, but still suffer from a tendency to strictly follow instructions, often causing them to hallucinate confident responses even when faced with unclear or anomalous inputs. Remarkably, for natural images such as ImageNet, this limitation can be overcome without pipeline modifications: simply prompting models to abstain from uncertain predictions enables significant reliability gains, achieving near-perfect robustness in several settings. However, for domain-specific tasks such as galaxy morphology classification, a lack of specialized knowledge prevents reliable uncertainty estimation. Finally, we propose a novel mechanism based on caption diversity to reveal a model's internal uncertainty, enabling practitioners to predict when models will successfully abstain without relying on labeled data.
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